Pre-trained language models (PLMs) like BERT [1]} and RoBERTA [2]} are
great unsupervised textual representations.
Several recent efforts adapt PLMs for the domains of interest by
self-supervised fine-tuning on unsupervised domain data, which has shown
to be promising in several scenarios
[3]}, [4]}, [5]}, [6]}.
We follow that and tune BERT on SE domains to obtain richer representations for the task of DQD.
